Target detection is a basic research topic in the field of computer vision, it has wide application prospect in many aspects such as face recognition, safety monitoring, and dynamic tracking. Target detection refers to detecting and identifying a specific target (such as a pedestrian) for any given image, and returning location and size information of the target, for example, outputting a border box that surrounds the target. Target detection is a complex and challenging mode detection problem, internal changes such as detail change of the target, occlusion, and external condition changes such as imaging angle, light effect, focal length of imaging device, imaging distance, image access difference, both will lead to difficulties in target detection and reduce accuracy.
Neural network is a large-scale, multi-parameter optimization tool. Depending on a lot of training data, neural network can learn hidden features that are difficult to summarize in the data, thus completing a number of complex tasks, such as face detection, picture classification, object detection, action tracking, natural language translation. Neural network has been widely used in the field of artificial intelligence. At present, the most widely used neural network in target detection, such as pedestrian detection, is convolutional neural network. There are two main problems that plague the current pedestrian target detection method: first, generation of a large number of “false positive” detection results, that is, a non-target area is marked as a target; second, incapability of automatically detecting some targets from the neural network due to light, target gestures and other effects. This is because during training and detection of the neural network for target detection, a position of the target in the picture is always generated directly, without fully considering division of this process and iterative training for the network, nor considering other factors that can assist in training and improving detection accuracy.